plot_intramodular_network: Function to visualize intramodular regulatory network

In jiang-junyao/IReNA: IReNA

Function to visualize intramodular regulatory network

Description

This function integrate R package igraph to visualize consequence of regulatory network analysis

Usage

plot_intramodular_network(
  TFs_list,
  enrichment = NULL,
  layout = "circle",
  group.cols = NULL,
  title.name = NULL,
  vertex.size = 25,
  vertex.label.color = "black",
  edge.label.color = "black",
  legend = TRUE,
  vertex.label.cex = 1.1,
  vertex.label.family = "ArialMT",
  frame.color = "white",
  arrow.size = 0.5,
  arrow.width = 0.8,
  edge.width = 2.5,
  edge.curved = 0,
  edge.color = c("#FDD1B0", "#B3B3B3"),
  vertex.label.degree = 0.35,
  vertex.label.dist = -6
)

Arguments

TFs_list	TFs_list generated by network_analysis
enrichment	Enrichment analysis results of genes in each module, generated by enrich_module. If it is not NULL, when plotting the Intramodular Network, the functions enriched by each module will be plotted in the diagram
layout	the layout to display the network, options: 'grid','sphere', 'circle','random'
group.cols	colors for group in network
title.name	the name of the title
vertex.size	vertex size
vertex.label.color	ertex label color
edge.label.color	edge label color
legend	logic, indicating whether to show the legend
vertex.label.cex	The label size of vertex
vertex.label.family	vertex label family
frame.color	frame.color
arrow.size	arrow size
arrow.width	arrow width
edge.width	edge width
edge.curved	edge curvature
edge.color	edge color. You need to input two colors, first one indicate 'Positive' regulation, second one indicate 'Negative' regulation.
vertex.label.degree	The position of the label in relation to the vertex (This parameter is valid only when the enrichment does not equal null)
vertex.label.dist	Distance between the label and the vertex (This parameter is valid only when the enrichment does not equal null)

Value

figure

Examples

#'
load(system.file("extdata", "test_clustering.rda", package = "IReNA"))
Kmeans_clustering <- add_ENSID(test_clustering, Spec1 = "Hs")
cor0.6 <- get_cor(Kmeans_clustering, Tranfac201803_Hs_MotifTFsF, 0.6, start_column=3)
TFs_list <- network_analysis(cor0.6,Kmeans_clustering)
#enrichment <- enrich_module(Kmeans_cluster_Ens, org.Hs.eg.db, 'KEGG')
#plot_intramodular_network(TFs_list,enrichment,layout = 'random')

jiang-junyao/IReNA documentation built on May 2, 2024, 6:54 a.m.